Clinical case study meets population cohort: identification of a BRCA1 pathogenic founder variant in Orcadians.

We multiply ascertained the BRCA1 pathogenic missense variant c.5207T > C; p.Val1736Ala (V1736A) in clinical investigation of breast and ovarian cancer families from Orkney in the Northern Isles of Scotland, UK. We sought to investigate the frequency and clinical relevance of this variant in those of Orcadian ancestry as an exemplar of the value of population cohorts in clinical care, especially in isolated populations. Oral history and birth, marriage and death registrations indicated genealogical linkage of the clinical cases to ancestors from the Isle of Westray, Orkney. Further clinical cases were identified through targeted testing for V1736A in women of Orcadian ancestry attending National Health Service (NHS) genetic clinics for breast and ovarian cancer family risk assessments. The variant segregates with female breast and ovarian cancer in clinically ascertained cases. Separately, exome sequence data from 2088 volunteer participants with three or more Orcadian grandparents, in the ORCADES research cohort, was interrogated to estimate the population prevalence of V1736A in Orcadians. The effects of the variant were assessed using Electronic Health Record (EHR) linkage. Twenty out of 2088 ORCADES research volunteers (~1%) carry V1736A, with a common haplotype around the variant. This allele frequency is ~480-fold higher than in UK Biobank participants. Cost-effectiveness of population screening for BRCA1 founder pathogenic variants has been demonstrated at a carrier frequency below the ~1% observed here. Thus we suggest that Orcadian women should be offered testing for the BRCA1 V1736A founder pathogenic variant, starting with those with known Westray ancestry.

Functional characterization of a novel p.Ser76Thr variant in IGFBP4 that associates with body mass index in American Indians.

Insulin-like growth factor binding protein 4 (IGFBP4) is involved in adipogenesis, and IGFBP4 null mice have decreased body fat through decreased PPAR-γ expression. In the current study, we assessed whether variation in the IGFBP4 coding region influences body mass index (BMI) in American Indians who are disproportionately affected by obesity. Whole exome sequence data from a population-based sample of 6779 American Indians with longitudinal measures of BMI were used to identify variation in IGFBP4 that associated with BMI. A novel variant that predicts a p.Ser76Thr in IGFBP4 (Thr-allele frequency = 0.02) was identified which associated with the maximum BMI measured during adulthood (BMI 39.8 kg/m2 for Thr-allele homozygotes combined with heterozygotes vs. 36.2 kg/m2 for Ser-allele homozygotes, ß = 6.7% per Thr-allele, p = 8.0 × 10-5, adjusted for age, sex, birth-year and the first five genetic principal components) and the maximum age- and sex-adjusted BMI z-score measured during childhood/adolescence (z-score 0.70 SD for Thr-allele heterozygotes vs. 0.32 SD for Ser-allele homozygotes, ß = 0.37 SD per Thr-allele, p = 8.8 × 10-6). In vitro functional studies showed that IGFBP4 with the Thr-allele (BMI-increasing) had a 55% decrease (p = 0.0007) in FOXO-induced transcriptional activity, reflecting increased activation of the PI3K/AKT pathway mediated through increased IGF signaling. Over-expression and knock-down of IGFBP4 in OP9 cells during differentiation showed that IGFBP4 upregulates adipogenesis through PPARγ, CEBPα, AGPAT2 and SREBP1 expression. We propose that this American Indian specific variant in IGFBP4 affects obesity via an increase of IGF signaling.

A missense variant Arg611Cys in LIPE which encodes hormone sensitive lipase decreases lipolysis and increases risk of type 2 diabetes in American Indians.

AIMS: Hormone sensitive lipase (HSL), encoded by the LIPE gene, is involved in lipolysis. Based on prior animal and human studies, LIPE was analysed as a candidate gene for the development of type 2 diabetes (T2D) in a community-based sample of American Indians. MATERIALS AND METHODS: Whole-exome sequence data from 6782 participants with longitudinal clinical measures were used to identify variation in LIPE. RESULTS: Amongst the 16 missense variants identified, an Arg611Cys variant (rs34052647; Cys-allele frequency = 0.087) significantly associated with T2D (OR [95% CI] = 1.38 [1.17-1.64], p = 0.0002, adjusted for age, sex, birth year, and the first five genetic principal components) and an earlier onset age of T2D (HR = 1.22 [1.09-1.36], p = 0.0005). This variant was further analysed for quantitative traits related to T2D. Amongst non-diabetic American Indians, those with the T2D risk Cys-allele had increased insulin levels during an oral glucose tolerance test (0.07 SD per Cys-allele, p = 0.04) and a mixed meal test (0.08 log10 µU/ml per Cys-allele, p = 0.003), and had increased lipid oxidation rates post-absorptively and during insulin infusion (0.07 mg [kg estimated metabolic body size {EMBS}]-1  min-1 per Cys-allele for both, p = 0.01 and 0.009, respectively), compared to individuals with the non-risk Arg-allele. In vitro functional studies showed that cells expressing the Cys-allele had a 17.2% decrease in lipolysis under isoproterenol stimulation (p = 0.03) and a 21.3% decrease in lipase enzyme activity measured by using p-nitrophenyl butyrate as a substrate (p = 0.04) compared to the Arg-allele. CONCLUSION: The Arg611Cys variant causes a modest impairment in lipolysis, thereby affecting glucose homoeostasis and risk of T2D.

Biallelic truncating variants in the muscular A-type lamin-interacting protein (MLIP) gene cause myopathy with hyperCKemia.

BACKGROUND AND PURPOSE: Muscular A-type lamin-interacting protein (MLIP) is most abundantly expressed in cardiac and skeletal muscle. In vitro and animal studies have shown its regulatory role in myoblast differentiation and in organization of myonuclear positioning in skeletal muscle, as well as in cardiomyocyte adaptation and cardiomyopathy. We report the association of biallelic truncating variation in the MLIP gene with human disease in five individuals from two unrelated pedigrees. METHODS: Clinical evaluation and exome sequencing were performed in two unrelated families with elevated creatine kinase level. RESULTS: Family 1. A 6-year-old girl born to consanguineous parents of Arab-Muslim origin presented with myalgia, early fatigue after mild-to-moderate physical exertion, and elevated creatine kinase levels up to 16,000 U/L. Exome sequencing revealed a novel homozygous nonsense variant, c.2530C>T; p.Arg844Ter, in the MLIP gene. Family 2. Three individuals from two distantly related families of Old Order Amish ancestry presented with elevated creatine kinase levels, one of whom also presented with abnormal electrocardiography results. On exome sequencing, all showed homozygosity for a novel nonsense MLIP variant c.1825A>T; p.Lys609Ter. Another individual from this pedigree, who had sinus arrhythmia and for whom creatine kinase level was not available, was also homozygous for this variant. CONCLUSIONS: Our findings suggest that biallelic truncating variants in MLIP result in myopathy characterized by hyperCKemia. Moreover, these cases of MLIP-related disease may indicate that at least in some instances this condition is associated with muscle decompensation and fatigability during low-to-moderate intensity muscle exertion as well as possible cardiac involvement.

Genetic and functional evidence links a missense variant in B4GALT1 to lower LDL and fibrinogen.

Increased blood levels of low-density lipoprotein cholesterol (LDL-C) and fibrinogen are independent risk factors for cardiovascular disease. We identified associations between an Amish-enriched missense variant (p.Asn352Ser) in a functional domain of beta-1,4-galactosyltransferase 1 (B4GALT1) and 13.9 milligrams per deciliter lower LDL-C (P = 4.1 × 10­19) and 29 milligrams per deciliter lower plasma fibrinogen (P = 1.3 × 10­5). B4GALT1 gene­based analysis in 544,955 subjects showed an association with decreased coronary artery disease (odds ratio = 0.64, P = 0.006). The mutant protein had 50% lower galactosyltransferase activity compared with the wild-type protein. N-linked glycan profiling of human serum found serine 352 allele to be associated with decreased galactosylation and sialylation of apolipoprotein B100, fibrinogen, immunoglobulin G, and transferrin. B4galt1 353Ser knock-in mice showed decreases in LDL-C and fibrinogen. Our findings suggest that targeted modulation of protein galactosylation may represent a therapeutic approach to decreasing cardiovascular disease.

The burden of pathogenic variants in clinically actionable genes in a founder population.

Founder populations may be enriched with certain genetic variants of high clinical impact compared to nonfounder populations due to bottleneck events and genetic drift. Using exome sequencing (ES), we quantified the load of pathogenic variants that may be clinically actionable in 6136 apparently healthy adults living in the Lancaster, PA Old Order Amish settlement. We focused on variants in 78 genes deemed clinically actionable by the American College of Medical Genetics and Genomics (ACMG) or Geisinger's MyCode Health Initiative. ES revealed 3191 total variants among these genes including 480 nonsynonymous variants. After quality control and filtering, we applied the ACMG/AMP guidelines for variant interpretation and classified seven variants, across seven genes, as either pathogenic or likely pathogenic. Through genetic drift, all seven variants, are highly enriched in the Amish compared to nonfounder populations. In total, 14.7% of Lancaster Amish individuals carry at least one of these variants, largely explained by the 13% who harbor a copy of a single variant in APOB. Other studies report combined frequencies of pathogenic/likely pathogenic (P/LP) variants in actionable genes between 2.0% and 6.2% in outbred populations. The Amish population harbors fewer actionable variants compared to similarly characterized nonfounder populations but have a higher frequency of each variant identified, offering opportunities for efficient and cost-effective targeted precision medicine.

Genome-wide association analysis of serum alanine and aspartate aminotransferase, and the modifying effects of BMI in 388k European individuals.

Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are biomarkers for liver health. Here we report the largest genome-wide association analysis to date of serum ALT and AST levels in over 388k people of European ancestry from UK biobank and DiscovEHR. Eleven million imputed markers with a minor allele frequency (MAF) ≥ 0.5% were analyzed. Overall, 300 ALT and 336 AST independent genome-wide significant associations were identified. Among them, 81 ALT and 61 AST associations are reported for the first time. Genome-wide interaction study identified 9 ALT and 12 AST independent associations significantly modified by body mass index (BMI), including several previously reported potential liver disease therapeutic targets, for example, PNPLA3, HSD17B13, and MARC1. While further work is necessary to understand the effect of ALT and AST-associated variants on liver disease, the weighted burden of significant BMI-modified signals is significantly associated with liver disease outcomes. In summary, this study identifies genetic associations which offer an important step forward in understanding the genetic architecture of serum ALT and AST levels. Significant interactions between BMI and genetic loci not only highlight the important role of adiposity in liver damage but also shed light on the genetic etiology of liver disease in obese individuals.

Genome-wide survey of parent-of-origin-specific associations across clinical traits derived from electronic health records.

Parent-of-origin (PoO) effects refer to the differential phenotypic impacts of genetic variants dependent on their parental inheritance due to imprinting. While PoO effects can influence complex traits, they may be poorly captured by models that do not differentiate the parental origin of the variant. The aim of this study was to conduct a genome-wide screen for PoO effects on a broad range of clinical traits derived from electronic health records (EHR) in the DiscovEHR study enriched with familial relationships. Using pairwise kinship estimates from genetic data and demographic data, we identified 22,051 offspring among 134,049 individuals in the DiscovEHR study. PoO of ~9 million variants was assigned in the offspring by comparing offspring and parental genotypes and haplotypes. We then performed genome-wide PoO association analyses across 154 quantitative and 611 binary traits extracted from EHR. Of the 732 significant PoO associations identified (p < 5 × 10-8), we attempted to replicate 274 PoO associations in the UK Biobank study with 5,015 offspring and replicated 9 PoO associations (p < 0.05). In summary, our study implements a bioinformatic and statistical approach to examine PoO effects genome-wide in a large population study enriched with familial relationships and systematically characterizes PoO effects on hundreds of clinical traits derived from EHR. Our results suggest that, while the statistical power to detect PoO effects remains modest yet, accurately modeling PoO effects has the potential to find new associations that may have been missed by the standard additive model, further enhancing the mechanistic understanding of genetic influence on complex traits.

Leveraging phenotypic variability to identify genetic interactions in human phenotypes.

Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

Exome sequencing and characterization of 49,960 individuals in the UK Biobank.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

Assessment of the potential role of natural selection in type 2 diabetes and related traits across human continental ancestry groups: comparison of phenotypic with genotypic divergence.

AIMS/HYPOTHESIS: Prevalence of type 2 diabetes differs among human ancestry groups, and many hypotheses invoke differential natural selection to account for these differences. We sought to assess the potential role of differential natural selection across major continental ancestry groups for diabetes and related traits, by comparison of genetic and phenotypic differences. METHODS: This was a cross-sectional comparison among 734 individuals from an urban sample (none of whom was more closely related to another than third-degree relatives), including 83 African Americans, 523 American Indians and 128 European Americans. Participants were not recruited based on diabetes status or other traits. BMI was calculated, and diabetes was diagnosed by a 75 g oral glucose tolerance test. In those with normal glucose tolerance (n = 434), fasting insulin and 30 min post-load insulin, adjusted for 30 min glucose, were taken as measures of insulin resistance and secretion, respectively. Whole exome sequencing was performed, resulting in 97,388 common (minor allele frequency ≥ 5%) variants; the coancestry coefficient (FST) was calculated across all markers as a measure of genetic divergence among ancestry groups. The phenotypic divergence index (PST) was also calculated from the phenotypic differences and heritability (which was estimated from genetic relatedness calculated empirically across all markers in 761 American Indian participants prior to the exclusion of close relatives). Under evolutionary neutrality, the expectation is PST = FST, while for traits under differential selection PST is expected to be significantly greater than FST. A bootstrap procedure was used to test the hypothesis PST = FST. RESULTS: With adjustment for age and sex, prevalence of type 2 diabetes was 34.0% in American Indians, 12.4% in African Americans and 10.4% in European Americans (p = 2.9 × 10-10 for difference among groups). Mean BMI was 36.3, 33.4 and 33.0 kg/m2, respectively (p = 1.9 × 10-7). Mean fasting insulin was 63.8, 48.4 and 45.2 pmol/l (p = 9.2 × 10-5), while mean 30 min insulin was 559.8, 553.5 and 358.8 pmol/l, respectively (p = 5.7 × 10-8). FST across all markers was 0.130, while PST for liability to diabetes, adjusted for age and sex, was 0.149 (p = 0.35 for difference with FST). PST was 0.094 for BMI (p = 0.54), 0.095 for fasting insulin (p = 0.54) and 0.216 (p = 0.18) for 30 min insulin. For type 2 diabetes and BMI, the maximum divergence between populations was observed between American Indians and European Americans (PST-MAX = 0.22, p = 0.37, and PST-MAX = 0.14, p = 0.61), which suggests that a relatively modest 22% or 14% of the genetic variance, respectively, can potentially be explained by differential selection (assuming the absence of neutral drift). CONCLUSIONS/INTERPRETATION: These analyses suggest that while type 2 diabetes and related traits differ significantly among continental ancestry groups, the differences are consistent with neutral expectations based on heritability and genetic distances. While these analyses do not exclude a modest role for natural selection, they do not support the hypothesis that differential natural selection is necessary to explain the phenotypic differences among these ancestry groups. Graphical abstract.

Exome Sequencing Identifies A Nonsense Variant in DAO Associated With Reduced Energy Expenditure in American Indians.

BACKGROUND: Obesity and energy expenditure (EE) are heritable and genetic variants influencing EE may contribute to the development of obesity. We sought to identify genetic variants that affect EE in American Indians, an ethnic group with high prevalence of obesity. METHODS: Whole-exome sequencing was performed in 373 healthy Pima Indians informative for 24-hour EE during energy balance. Genetic association analyses of all high-quality exonic variants (≥5 carriers) was performed, and those predicted to be damaging were prioritized. RESULTS: Rs752074397 introduces a premature stop codon (Cys264Ter) in DAO and demonstrated the strongest association for 24-hour EE, where the Ter allele associated with substantially lower 24-hour EE (mean lower by 268 kcal/d) and sleeping EE (by 135 kcal/d). The Ter allele has a frequency = 0.5% in Pima Indians, whereas is extremely rare in most other ethnic groups (frequency < 0.01%). In vitro functional analysis showed reduced protein levels for the truncated form of DAO consistent with increased protein degradation. DAO encodes D-amino acid oxidase, which is involved in dopamine synthesis which might explain its role in modulating EE. CONCLUSION: Our results indicate that a nonsense mutation in DAO may influence EE in American Indians. Identification of variants that influence energy metabolism may lead to new pathways to treat human obesity. CLINICAL TRIAL REGISTRATION NUMBER: NCT00340132.

Characterization of Exome Variants and Their Metabolic Impact in 6,716 American Indians from the Southwest US.

Applying exome sequencing to populations with unique genetic architecture has the potential to reveal novel genes and variants associated with traits and diseases. We sequenced and analyzed the exomes of 6,716 individuals from a Southwestern American Indian (SWAI) population with well-characterized metabolic traits. We found that the SWAI population has distinct allelic architecture compared to populations of European and East Asian ancestry, and there were many predicted loss-of-function (pLOF) and nonsynonymous variants that were highly enriched or private in the SWAI population. We used pLOF and nonsynonymous variants in the SWAI population to evaluate gene-burden associations of candidate genes from European genome-wide association studies (GWASs) for type 2 diabetes, body mass index, and four major plasma lipids. We found 19 significant gene-burden associations for 11 genes, providing additional evidence for prioritizing candidate effector genes of GWAS signals. Interestingly, these associations were mainly driven by pLOF and nonsynonymous variants that are unique or highly enriched in the SWAI population. Particularly, we found four pLOF or nonsynonymous variants in APOB, APOE, PCSK9, and TM6SF2 that are private or enriched in the SWAI population and associated with low-density lipoprotein (LDL) cholesterol levels. Their large estimated effects on LDL cholesterol levels suggest strong impacts on protein function and potential clinical implications of these variants in cardiovascular health. In summary, our study illustrates the utility and potential of exome sequencing in genetically unique populations, such as the SWAI population, to prioritize candidate effector genes within GWAS loci and to find additional variants in known disease genes with potential clinical impact.

U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies.

SUMMARY: Despite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations. AVAILABILITY AND IMPLEMENTATION: U-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic inactivation of ANGPTL4 improves glucose homeostasis and is associated with reduced risk of diabetes.

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85-0.92, p = 6.3 × 10-10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49-0.99, p = 0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D.

Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes.

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in line with expectations given the underlying population and ascertainment approach. For example, within DiscovEHR we identified ∼66,000 close (first- and second-degree) relationships, involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships, given that DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees by using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in ∼1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations, including a tandem duplication that occurs in LDLR and causes familial hypercholesterolemia, through reconstructed pedigrees. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population-genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease.

BACKGROUND: Loss-of-function variants in the angiopoietin-like 3 gene (ANGPTL3) have been associated with decreased plasma levels of triglycerides, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol. It is not known whether such variants or therapeutic antagonism of ANGPTL3 are associated with a reduced risk of atherosclerotic cardiovascular disease. METHODS: We sequenced the exons of ANGPTL3 in 58,335 participants in the DiscovEHR human genetics study. We performed tests of association for loss-of-function variants in ANGPTL3 with lipid levels and with coronary artery disease in 13,102 case patients and 40,430 controls from the DiscovEHR study, with follow-up studies involving 23,317 case patients and 107,166 controls from four population studies. We also tested the effects of a human monoclonal antibody, evinacumab, against Angptl3 in dyslipidemic mice and against ANGPTL3 in healthy human volunteers with elevated levels of triglycerides or LDL cholesterol. RESULTS: In the DiscovEHR study, participants with heterozygous loss-of-function variants in ANGPTL3 had significantly lower serum levels of triglycerides, HDL cholesterol, and LDL cholesterol than participants without these variants. Loss-of-function variants were found in 0.33% of case patients with coronary artery disease and in 0.45% of controls (adjusted odds ratio, 0.59; 95% confidence interval, 0.41 to 0.85; P=0.004). These results were confirmed in the follow-up studies. In dyslipidemic mice, inhibition of Angptl3 with evinacumab resulted in a greater decrease in atherosclerotic lesion area and necrotic content than a control antibody. In humans, evinacumab caused a dose-dependent placebo-adjusted reduction in fasting triglyceride levels of up to 76% and LDL cholesterol levels of up to 23%. CONCLUSIONS: Genetic and therapeutic antagonism of ANGPTL3 in humans and of Angptl3 in mice was associated with decreased levels of all three major lipid fractions and decreased odds of atherosclerotic cardiovascular disease. (Funded by Regeneron Pharmaceuticals and others; ClinicalTrials.gov number, NCT01749878 .).

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

Inactivating Variants in ANGPTL4 and Risk of Coronary Artery Disease.

BACKGROUND: Higher-than-normal levels of circulating triglycerides are a risk factor for ischemic cardiovascular disease. Activation of lipoprotein lipase, an enzyme that is inhibited by angiopoietin-like 4 (ANGPTL4), has been shown to reduce levels of circulating triglycerides. METHODS: We sequenced the exons of ANGPTL4 in samples obtain from 42,930 participants of predominantly European ancestry in the DiscovEHR human genetics study. We performed tests of association between lipid levels and the missense E40K variant (which has been associated with reduced plasma triglyceride levels) and other inactivating mutations. We then tested for associations between coronary artery disease and the E40K variant and other inactivating mutations in 10,552 participants with coronary artery disease and 29,223 controls. We also tested the effect of a human monoclonal antibody against ANGPTL4 on lipid levels in mice and monkeys. RESULTS: We identified 1661 heterozygotes and 17 homozygotes for the E40K variant and 75 participants who had 13 other monoallelic inactivating mutations in ANGPTL4. The levels of triglycerides were 13% lower and the levels of high-density lipoprotein (HDL) cholesterol were 7% higher among carriers of the E40K variant than among noncarriers. Carriers of the E40K variant were also significantly less likely than noncarriers to have coronary artery disease (odds ratio, 0.81; 95% confidence interval, 0.70 to 0.92; P=0.002). K40 homozygotes had markedly lower levels of triglycerides and higher levels of HDL cholesterol than did heterozygotes. Carriers of other inactivating mutations also had lower triglyceride levels and higher HDL cholesterol levels and were less likely to have coronary artery disease than were noncarriers. Monoclonal antibody inhibition of Angptl4 in mice and monkeys reduced triglyceride levels. CONCLUSIONS: Carriers of E40K and other inactivating mutations in ANGPTL4 had lower levels of triglycerides and a lower risk of coronary artery disease than did noncarriers. The inhibition of Angptl4 in mice and monkeys also resulted in corresponding reductions in these values. (Funded by Regeneron Pharmaceuticals.).

INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.